diff --git a/src/guidellm/__init__.py b/src/guidellm/__init__.py
index e5620188..b10b4455 100644
--- a/src/guidellm/__init__.py
+++ b/src/guidellm/__init__.py
@@ -6,6 +6,7 @@
 # flake8: noqa
 
 import os
+
 import transformers  # type: ignore
 
 os.environ["TOKENIZERS_PARALLELISM"] = "false"  # Silence warnings for tokenizers
diff --git a/src/guidellm/backend/__init__.py b/src/guidellm/backend/__init__.py
index 875e319e..13910180 100644
--- a/src/guidellm/backend/__init__.py
+++ b/src/guidellm/backend/__init__.py
@@ -1,5 +1,6 @@
 from .base import Backend, BackendEngine, BackendEnginePublic, GenerativeResponse
 from .openai import OpenAIBackend
+from .aiohttp import AiohttpBackend
 
 __all__ = [
     "Backend",
@@ -7,4 +8,5 @@
     "BackendEnginePublic",
     "GenerativeResponse",
     "OpenAIBackend",
+    "AiohttpBackend"
 ]
diff --git a/src/guidellm/backend/aiohttp.py b/src/guidellm/backend/aiohttp.py
new file mode 100644
index 00000000..fbbd9715
--- /dev/null
+++ b/src/guidellm/backend/aiohttp.py
@@ -0,0 +1,180 @@
+import base64
+import io
+from typing import AsyncGenerator, Dict, List, Optional
+from loguru import logger
+
+import aiohttp
+import json
+
+from guidellm.backend.base import Backend, GenerativeResponse
+from guidellm.config import settings
+from guidellm.core import TextGenerationRequest
+
+__all__ = ["AiohttpBackend"]
+
+@Backend.register("aiohttp_server")
+class AiohttpBackend(Backend):
+    """
+    An aiohttp-based backend implementation for LLM requests.
+
+    This class provides an interface to communicate with a server hosting
+    an LLM API using aiohttp for asynchronous requests.
+    """
+
+    def __init__(
+        self,
+        openai_api_key: Optional[str] = None,
+        target: Optional[str] = None,
+        model: Optional[str] = None,
+        timeout: Optional[float] = None,
+        **request_args,
+    ):
+        self._request_args: Dict = request_args        
+        self._api_key: str = openai_api_key or settings.aiohttp.api_key
+
+        if not self._api_key:
+            err = ValueError(
+                "`GUIDELLM__AIOHTTP__API_KEY` environment variable or "
+                "--openai-api-key CLI parameter must be specified for the "
+                "aiohttp backend."
+            )
+            logger.error("{}", err)
+            raise err
+
+        base_url = target or settings.aiohttp.base_url
+        self._api_url = f"{base_url}/chat/completions"
+
+        if not base_url:
+            err = ValueError(
+                "`GUIDELLM__AIOHTTP__BASE_URL` environment variable or "
+                "target parameter must be specified for the OpenAI backend."
+            )
+            logger.error("{}", err)
+            raise err
+
+        self._timeout = aiohttp.ClientTimeout(total=timeout or settings.request_timeout)
+        self._model = model
+
+        super().__init__(type_="aiohttp_backend", target=base_url, model=self._model)
+        logger.info("aiohttp {} Backend listening on {}", self._model, base_url)
+
+    async def make_request(
+        self,
+        request: TextGenerationRequest,
+    ) -> AsyncGenerator[GenerativeResponse, None]:
+        """
+        Make a request to the aiohttp backend.
+
+        Sends a prompt to the LLM server and streams the response tokens.
+
+        :param request: The text generation request to submit.
+        :type request: TextGenerationRequest
+        :yield: A stream of GenerativeResponse objects.
+        :rtype: AsyncGenerator[GenerativeResponse, None]
+        """
+
+        async with aiohttp.ClientSession(timeout=self._timeout) as session:
+            logger.debug("Making request to aiohttp backend with prompt: {}", request.prompt)
+
+            request_args = {}
+            if request.output_token_count is not None:
+                request_args.update(
+                    {
+                        "max_completion_tokens": request.output_token_count,
+                        "stop": None,
+                        "ignore_eos": True,
+                    }
+                )
+            elif settings.aiohttp.max_gen_tokens and settings.aiohttp.max_gen_tokens > 0:
+                request_args.update(
+                    {
+                        "max_tokens": settings.aiohttp.max_gen_tokens,
+                    }
+                )
+
+            request_args.update(self._request_args)
+
+            messages = self._build_messages(request)
+
+            payload = {
+                "model": self._model,
+                "messages": messages,
+                "stream": True,
+                **request_args,
+            }
+
+            headers = {
+                "Content-Type": "application/json",
+                "Authorization": f"Bearer {self._api_key}",
+            }
+
+            try:
+                async with session.post(url=self._api_url, json=payload, headers=headers) as response:
+                    if response.status != 200:
+                        error_message = await response.text()
+                        logger.error("Request failed: {} - {}", response.status, error_message)
+                        raise Exception(f"Failed to generate response: {error_message}")
+
+                    token_count = 0
+                    async for chunk_bytes in response.content:
+                        chunk_bytes = chunk_bytes.strip()
+                        if not chunk_bytes:
+                            continue
+
+                        chunk = chunk_bytes.decode("utf-8").removeprefix("data: ")
+                        if chunk == "[DONE]":
+                            # Final response
+                            yield GenerativeResponse(
+                                type_="final",
+                                prompt=request.prompt,
+                                output_token_count=token_count,
+                                prompt_token_count=request.prompt_token_count,
+                            )
+                        else:
+                            # Intermediate token response
+                            token_count += 1
+                            data = json.loads(chunk)
+                            delta = data["choices"][0]["delta"]
+                            token = delta["content"]
+                            yield GenerativeResponse(
+                                type_="token_iter",
+                                add_token=token,
+                                prompt=request.prompt,
+                                output_token_count=token_count,
+                                prompt_token_count=request.prompt_token_count,
+                            )
+            except Exception as e:
+                logger.error("Error while making request: {}", e)
+                raise
+
+    def available_models(self) -> List[str]:
+        """
+        Retrieve a list of available models from the server.
+        """
+        # This could include an API call to `self._api_url/models` if the server supports it.
+        logger.warning("Fetching available models is not implemented for aiohttp backend.")
+        return []
+
+    def validate_connection(self):
+        """
+        Validate the connection to the backend server.
+        """
+        logger.info("Connection validation is not explicitly implemented for aiohttp backend.")
+
+    def _build_messages(self, request: TextGenerationRequest) -> Dict:
+        if request.number_images == 0:
+            messages = [{"role": "user", "content": request.prompt}]
+        else:
+            content = []
+            for image in request.images:
+                stream = io.BytesIO()
+                im_format = image.image.format or "PNG"
+                image.image.save(stream, format=im_format)
+                im_b64 = base64.b64encode(stream.getvalue()).decode("utf-8")
+                image_url = {"url": f"data:image/{im_format.lower()};base64,{im_b64}"}
+                content.append({"type": "image_url", "image_url": image_url})
+
+            content.append({"type": "text", "text": request.prompt})
+            messages = [{"role": "user", "content": content}]
+
+        return messages
diff --git a/src/guidellm/backend/base.py b/src/guidellm/backend/base.py
index d71c5f66..a1658594 100644
--- a/src/guidellm/backend/base.py
+++ b/src/guidellm/backend/base.py
@@ -15,7 +15,7 @@
 __all__ = ["Backend", "BackendEngine", "BackendEnginePublic", "GenerativeResponse"]
 
 
-BackendEnginePublic = Literal["openai_server"]
+BackendEnginePublic = Literal["openai_server", "aiohttp_server"]
 BackendEngine = Union[BackendEnginePublic, Literal["test"]]
 
 
diff --git a/src/guidellm/backend/openai.py b/src/guidellm/backend/openai.py
index 8c83f914..9843fc1a 100644
--- a/src/guidellm/backend/openai.py
+++ b/src/guidellm/backend/openai.py
@@ -1,3 +1,5 @@
+import base64
+import io
 from typing import AsyncGenerator, Dict, List, Optional
 
 from loguru import logger
@@ -92,6 +94,9 @@ async def make_request(
                 {
                     "max_tokens": request.output_token_count,
                     "stop": None,
+                    "extra_body": {
+                        "ignore_eos": True,
+                    }
                 }
             )
         elif settings.openai.max_gen_tokens and settings.openai.max_gen_tokens > 0:
@@ -103,11 +108,11 @@ async def make_request(
 
         request_args.update(self._request_args)
 
+        messages = self._build_messages(request)
+
         stream = await self._async_client.chat.completions.create(
             model=self.model,
-            messages=[
-                {"role": "user", "content": request.prompt},
-            ],
+            messages=messages,
             stream=True,
             **request_args,
         )
@@ -167,3 +172,21 @@ def validate_connection(self):
         except Exception as error:
             logger.error("Failed to validate OpenAI connection: {}", error)
             raise error
+
+    def _build_messages(self, request: TextGenerationRequest) -> Dict:
+        if request.number_images == 0:
+            messages = [{"role": "user", "content": request.prompt}]
+        else:
+            content = []
+            for image in request.images:
+                stream = io.BytesIO()
+                im_format = image.image.format or "PNG"
+                image.image.save(stream, format=im_format)
+                im_b64 = base64.b64encode(stream.getvalue()).decode("utf-8")
+                image_url = {"url": f"data:image/{im_format.lower()};base64,{im_b64}"}
+                content.append({"type": "image_url", "image_url": image_url})
+
+            content.append({"type": "text", "text": request.prompt})
+            messages = [{"role": "user", "content": content}]
+
+        return messages
diff --git a/src/guidellm/config.py b/src/guidellm/config.py
index c3d950ec..a19a6244 100644
--- a/src/guidellm/config.py
+++ b/src/guidellm/config.py
@@ -90,6 +90,7 @@ class EmulatedDataSettings(BaseModel):
             "force_new_line_punctuation": True,
         }
     )
+    image_source: List[str] = "https://www.gutenberg.org/cache/epub/1342/pg1342-images.html"
 
 
 class OpenAISettings(BaseModel):
@@ -108,6 +109,9 @@ class OpenAISettings(BaseModel):
     max_gen_tokens: int = 4096
 
 
+class AiohttpSettings(OpenAISettings):
+    pass
+
 class ReportGenerationSettings(BaseModel):
     """
     Report generation settings for the application
@@ -152,6 +156,7 @@ class Settings(BaseSettings):
 
     # Request settings
     openai: OpenAISettings = OpenAISettings()
+    aiohttp: AiohttpSettings = AiohttpSettings()
 
     # Report settings
     report_generation: ReportGenerationSettings = ReportGenerationSettings()
diff --git a/src/guidellm/core/report.py b/src/guidellm/core/report.py
index b6791e45..c48eed56 100644
--- a/src/guidellm/core/report.py
+++ b/src/guidellm/core/report.py
@@ -147,19 +147,15 @@ def _create_benchmark_report_data_tokens_summary(
     for benchmark in report.benchmarks_sorted:
         table.add_row(
             _benchmark_rate_id(benchmark),
-            f"{benchmark.prompt_token_distribution.mean:.2f}",
+            f"{benchmark.prompt_token:.2f}",
             ", ".join(
                 f"{percentile:.1f}"
-                for percentile in benchmark.prompt_token_distribution.percentiles(
-                    [1, 5, 50, 95, 99]
-                )
+                for percentile in benchmark.prompt_token_percentiles
             ),
-            f"{benchmark.output_token_distribution.mean:.2f}",
+            f"{benchmark.output_token:.2f}",
             ", ".join(
                 f"{percentile:.1f}"
-                for percentile in benchmark.output_token_distribution.percentiles(
-                    [1, 5, 50, 95, 99]
-                )
+                for percentile in benchmark.output_token_percentiles
             ),
         )
     logger.debug("Created data tokens summary table for the report.")
@@ -181,7 +177,7 @@ def _create_benchmark_report_dist_perf_summary(
         "Benchmark",
         "Request Latency [1%, 5%, 10%, 50%, 90%, 95%, 99%] (sec)",
         "Time to First Token [1%, 5%, 10%, 50%, 90%, 95%, 99%] (ms)",
-        "Inter Token Latency [1%, 5%, 10%, 50%, 90% 95%, 99%] (ms)",
+        "Inter Token Latency [1%, 5%, 10%, 50%, 90%, 95%, 99%] (ms)",
         title="[magenta]Performance Stats by Benchmark[/magenta]",
         title_style="bold",
         title_justify="left",
@@ -193,21 +189,15 @@ def _create_benchmark_report_dist_perf_summary(
             _benchmark_rate_id(benchmark),
             ", ".join(
                 f"{percentile:.2f}"
-                for percentile in benchmark.request_latency_distribution.percentiles(
-                    [1, 5, 10, 50, 90, 95, 99]
-                )
+                for percentile in benchmark.request_latency_percentiles
             ),
             ", ".join(
                 f"{percentile * 1000:.1f}"
-                for percentile in benchmark.ttft_distribution.percentiles(
-                    [1, 5, 10, 50, 90, 95, 99]
-                )
+                for percentile in benchmark.time_to_first_token_percentiles
             ),
             ", ".join(
                 f"{percentile * 1000:.1f}"
-                for percentile in benchmark.itl_distribution.percentiles(
-                    [1, 5, 10, 50, 90, 95, 99]
-                )
+                for percentile in benchmark.inter_token_latency_percentiles
             ),
         )
     logger.debug("Created distribution performance summary table for the report.")
diff --git a/src/guidellm/core/request.py b/src/guidellm/core/request.py
index 4f7315c5..06d0f37c 100644
--- a/src/guidellm/core/request.py
+++ b/src/guidellm/core/request.py
@@ -1,9 +1,10 @@
 import uuid
-from typing import Any, Dict, Optional
+from typing import Any, Dict, List, Optional, Tuple
 
 from pydantic import Field
 
 from guidellm.core.serializable import Serializable
+from guidellm.utils import ImageDescriptor
 
 
 class TextGenerationRequest(Serializable):
@@ -16,6 +17,10 @@ class TextGenerationRequest(Serializable):
         description="The unique identifier for the request.",
     )
     prompt: str = Field(description="The input prompt for the text generation.")
+    images: Optional[List[ImageDescriptor]] = Field(
+        default=None,
+        description="Input images.",
+    )
     prompt_token_count: Optional[int] = Field(
         default=None,
         description="The number of tokens in the input prompt.",
@@ -29,6 +34,21 @@ class TextGenerationRequest(Serializable):
         description="The parameters for the text generation request.",
     )
 
+    @property
+    def number_images(self) -> int:
+        if self.images is None:
+            return 0
+        else:
+            return len(self.images)
+
+    @property
+    def image_resolution(self) -> List[Tuple[int, int]]:
+        if self.images is None:
+            return None
+        else:
+            return [im.size for im in self.images]
+
+
     def __str__(self) -> str:
         prompt_short = (
             self.prompt[:32] + "..."
@@ -41,4 +61,5 @@ def __str__(self) -> str:
             f"prompt={prompt_short}, prompt_token_count={self.prompt_token_count}, "
             f"output_token_count={self.output_token_count}, "
             f"params={self.params})"
+            f"image_resolution={self.image_resolution}"
         )
diff --git a/src/guidellm/core/result.py b/src/guidellm/core/result.py
index f218784c..aebd1763 100644
--- a/src/guidellm/core/result.py
+++ b/src/guidellm/core/result.py
@@ -2,7 +2,7 @@
 from typing import Any, Dict, List, Literal, Optional, Union
 
 from loguru import logger
-from pydantic import Field
+from pydantic import Field, computed_field
 
 from guidellm.core.distribution import Distribution
 from guidellm.core.request import TextGenerationRequest
@@ -221,6 +221,7 @@ def __iter__(self):
         """
         return iter(self.results)
 
+    @computed_field # type: ignore[misc]
     @property
     def request_count(self) -> int:
         """
@@ -231,6 +232,7 @@ def request_count(self) -> int:
         """
         return len(self.results)
 
+    @computed_field # type: ignore[misc]
     @property
     def error_count(self) -> int:
         """
@@ -241,6 +243,7 @@ def error_count(self) -> int:
         """
         return len(self.errors)
 
+    @computed_field # type: ignore[misc]
     @property
     def total_count(self) -> int:
         """
@@ -251,6 +254,7 @@ def total_count(self) -> int:
         """
         return self.request_count + self.error_count
 
+    @computed_field # type: ignore[misc]
     @property
     def start_time(self) -> Optional[float]:
         """
@@ -264,6 +268,7 @@ def start_time(self) -> Optional[float]:
 
         return self.results[0].start_time
 
+    @computed_field # type: ignore[misc]
     @property
     def end_time(self) -> Optional[float]:
         """
@@ -277,6 +282,7 @@ def end_time(self) -> Optional[float]:
 
         return self.results[-1].end_time
 
+    @computed_field # type: ignore[misc]
     @property
     def duration(self) -> float:
         """
@@ -290,6 +296,7 @@ def duration(self) -> float:
 
         return self.end_time - self.start_time
 
+    @computed_field # type: ignore[misc]
     @property
     def completed_request_rate(self) -> float:
         """
@@ -303,6 +310,7 @@ def completed_request_rate(self) -> float:
 
         return len(self.results) / self.duration
 
+    @computed_field # type: ignore[misc]
     @property
     def request_latency(self) -> float:
         """
@@ -332,6 +340,19 @@ def request_latency_distribution(self) -> Distribution:
             ]
         )
 
+    @computed_field # type: ignore[misc]
+    @property
+    def request_latency_percentiles(self) -> List[float]:
+        """
+        Get standard percentiles of request latency in seconds.
+
+        :return: List of percentile request latency in seconds
+        :rtype: List[float]
+        """
+        return self.request_latency_distribution.percentiles([1, 5, 10, 50, 90, 95, 99])
+
+
+    @computed_field # type: ignore[misc]
     @property
     def time_to_first_token(self) -> float:
         """
@@ -361,6 +382,20 @@ def ttft_distribution(self) -> Distribution:
             ]
         )
 
+    @computed_field # type: ignore[misc]
+    @property
+    def time_to_first_token_percentiles(self) -> List[float]:
+        """
+        Get standard percentiles for time taken to decode the first token
+        in milliseconds.
+
+        :return: List of percentile time taken to decode the first token
+        in milliseconds.
+        :rtype: List[float]
+        """
+        return self.ttft_distribution.percentiles([1, 5, 10, 50, 90, 95, 99])
+
+    @computed_field # type: ignore[misc]
     @property
     def inter_token_latency(self) -> float:
         """
@@ -388,6 +423,18 @@ def itl_distribution(self) -> Distribution:
             ]
         )
 
+    @computed_field # type: ignore[misc]
+    @property
+    def inter_token_latency_percentiles(self) -> List[float]:
+        """
+        Get standard percentiles for the time between tokens in milliseconds.
+
+        :return: List of percentiles for the average time between tokens.
+        :rtype: List[float]
+        """
+        return self.itl_distribution.percentiles([1, 5, 10, 50, 90, 95, 99])
+
+    @computed_field # type: ignore[misc]
     @property
     def output_token_throughput(self) -> float:
         """
@@ -403,6 +450,17 @@ def output_token_throughput(self) -> float:
 
         return total_tokens / self.duration
 
+    @computed_field # type: ignore[misc]
+    @property
+    def prompt_token(self) -> float:
+        """
+        Get the average number of prompt tokens.
+
+        :return: The average number of prompt tokens.
+        :rtype: float
+        """
+        return self.prompt_token_distribution.mean
+
     @property
     def prompt_token_distribution(self) -> Distribution:
         """
@@ -413,6 +471,28 @@ def prompt_token_distribution(self) -> Distribution:
         """
         return Distribution(data=[result.prompt_token_count for result in self.results])
 
+    @computed_field # type: ignore[misc]
+    @property
+    def prompt_token_percentiles(self) -> List[float]:
+        """
+        Get standard percentiles for number of prompt tokens.
+
+        :return: List of percentiles of number of prompt tokens.
+        :rtype: List[float]
+        """
+        return self.prompt_token_distribution.percentiles([1, 5, 50, 95, 99])
+
+    @computed_field # type: ignore[misc]
+    @property
+    def output_token(self) -> float:
+        """
+        Get the average number of output tokens.
+
+        :return: The average number of output tokens.
+        :rtype: float
+        """
+        return self.output_token_distribution.mean
+
     @property
     def output_token_distribution(self) -> Distribution:
         """
@@ -423,6 +503,18 @@ def output_token_distribution(self) -> Distribution:
         """
         return Distribution(data=[result.output_token_count for result in self.results])
 
+    @computed_field # type: ignore[misc]
+    @property
+    def output_token_percentiles(self) -> List[float]:
+        """
+        Get standard percentiles for number of output tokens.
+
+        :return: List of percentiles of number of output tokens.
+        :rtype: List[float]
+        """
+        return self.output_token_distribution.percentiles([1, 5, 50, 95, 99])
+
+    @computed_field # type: ignore[misc]
     @property
     def overloaded(self) -> bool:
         if (
diff --git a/src/guidellm/main.py b/src/guidellm/main.py
index 4016ecec..4748b12d 100644
--- a/src/guidellm/main.py
+++ b/src/guidellm/main.py
@@ -186,17 +186,17 @@ def generate_benchmark_report_cli(
 
 def generate_benchmark_report(
     target: str,
-    backend: BackendEnginePublic,
-    model: Optional[str],
     data: Optional[str],
     data_type: Literal["emulated", "file", "transformers"],
-    tokenizer: Optional[str],
-    rate_type: ProfileGenerationMode,
-    rate: Optional[float],
-    max_seconds: Optional[int],
-    max_requests: Union[Literal["dataset"], int, None],
-    output_path: str,
-    cont_refresh_table: bool,
+    backend: BackendEnginePublic="openai_server",
+    model: Optional[str]=None,
+    tokenizer: Optional[str]=None,
+    rate_type: ProfileGenerationMode="sweep",
+    rate: Optional[float]=None,
+    max_seconds: Optional[int]=120,
+    max_requests: Union[Literal["dataset"], int, None]=None,
+    output_path: str=None,
+    cont_refresh_table: bool=False,
 ) -> GuidanceReport:
     """
     Generate a benchmark report for a specified backend and dataset.
diff --git a/src/guidellm/request/emulated.py b/src/guidellm/request/emulated.py
index 7d481cb7..02f564a1 100644
--- a/src/guidellm/request/emulated.py
+++ b/src/guidellm/request/emulated.py
@@ -11,7 +11,7 @@
 from guidellm.config import settings
 from guidellm.core.request import TextGenerationRequest
 from guidellm.request.base import GenerationMode, RequestGenerator
-from guidellm.utils import clean_text, filter_text, load_text, split_text
+from guidellm.utils import clean_text, filter_text, load_images, load_text, split_text
 
 __all__ = ["EmulatedConfig", "EmulatedRequestGenerator", "EndlessTokens"]
 
@@ -30,6 +30,9 @@ class EmulatedConfig:
         generated_tokens_variance (Optional[int]): Variance for generated tokens.
         generated_tokens_min (Optional[int]): Minimum number of generated tokens.
         generated_tokens_max (Optional[int]): Maximum number of generated tokens.
+        images (Optional[int]): Number of images.
+        width (Optional[int]): Width of images.
+        height (Optional[int]): Height of images.
     """
 
     @staticmethod
@@ -47,7 +50,7 @@ def create_config(config: Optional[Union[str, Path, Dict]]) -> "EmulatedConfig":
         """
         if not config:
             logger.debug("Creating default configuration")
-            return EmulatedConfig(prompt_tokens=1024, generated_tokens=256)
+            return EmulatedConfig(prompt_tokens=1024, generated_tokens=256, images=0)
 
         if isinstance(config, dict):
             logger.debug("Loading configuration from dict: {}", config)
@@ -105,6 +108,10 @@ def create_config(config: Optional[Union[str, Path, Dict]]) -> "EmulatedConfig":
     generated_tokens_min: Optional[int] = None
     generated_tokens_max: Optional[int] = None
 
+    images: int = 0
+    width: int = None
+    height: int = None
+
     @property
     def prompt_tokens_range(self) -> Tuple[int, int]:
         """
@@ -327,6 +334,8 @@ def __init__(
             settings.emulated_data.filter_start,
             settings.emulated_data.filter_end,
         )
+        if self._config.images > 0:
+            self._images = load_images(settings.emulated_data.image_source, [self._config.width, self._config.height])
         self._rng = np.random.default_rng(random_seed)
 
         # NOTE: Must be after all the parameters since the queue population
@@ -355,6 +364,7 @@ def create_item(self) -> TextGenerationRequest:
         logger.debug("Creating new text generation request")
         target_prompt_token_count = self._config.sample_prompt_tokens(self._rng)
         prompt = self.sample_prompt(target_prompt_token_count)
+        images = self.sample_images()
         prompt_token_count = len(self.tokenizer.tokenize(prompt))
         output_token_count = self._config.sample_output_tokens(self._rng)
         logger.debug("Generated prompt: {}", prompt)
@@ -363,6 +373,7 @@ def create_item(self) -> TextGenerationRequest:
             prompt=prompt,
             prompt_token_count=prompt_token_count,
             output_token_count=output_token_count,
+            images=images,
         )
 
     def sample_prompt(self, tokens: int) -> str:
@@ -395,3 +406,11 @@ def sample_prompt(self, tokens: int) -> str:
                 right = mid
 
         return self._tokens.create_text(start_line_index, left)
+
+
+    def sample_images(self):
+        image_indices = self._rng.choice(
+            len(self._images), size=self._config.images, replace=False,
+        )
+
+        return [self._images[i] for i in image_indices]
diff --git a/src/guidellm/utils/__init__.py b/src/guidellm/utils/__init__.py
index 2fdd8ca8..eb4931bd 100644
--- a/src/guidellm/utils/__init__.py
+++ b/src/guidellm/utils/__init__.py
@@ -1,3 +1,4 @@
+from .images import ImageDescriptor, load_images
 from .injector import create_report, inject_data
 from .progress import BenchmarkReportProgress
 from .text import (
@@ -37,4 +38,6 @@
     "resolve_transformers_dataset_split",
     "split_lines_by_punctuation",
     "split_text",
+    "ImageDescriptor",
+    "load_images",
 ]
diff --git a/src/guidellm/utils/images.py b/src/guidellm/utils/images.py
new file mode 100644
index 00000000..fb66d432
--- /dev/null
+++ b/src/guidellm/utils/images.py
@@ -0,0 +1,80 @@
+from io import BytesIO
+from typing import List, Optional, Tuple
+from urllib.parse import urljoin
+
+import requests
+from bs4 import BeautifulSoup
+from loguru import logger
+from PIL import Image
+from pydantic import ConfigDict, Field, computed_field
+
+from guidellm.config import settings
+from guidellm.core.serializable import Serializable
+
+__all__ = ["load_images", "ImageDescriptor"]
+
+class ImageDescriptor(Serializable):
+    """
+    A class to represent image data in serializable format.
+    """
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
+    url: Optional[str] = Field(description="url address for image.")
+    image: Image.Image = Field(description="PIL image", exclude=True)
+    filename: Optional[int] = Field(
+        default=None,
+        description="Image filename.",
+    )
+
+    @computed_field # type: ignore[misc]
+    @property
+    def image_resolution(self) -> Tuple[int, int]:
+        if self.image is None:
+            return None
+        else:
+            return self.image.size
+
+
+def load_images(data: str, image_resolution: Optional[List[int]]) -> List[ImageDescriptor]:
+    """
+    Load an HTML file from a path or URL
+
+    :param data: the path or URL to load the HTML file from
+    :type data: Union[str, Path]
+    :return: Descriptor containing image url and the data in PIL.Image.Image format
+    :rtype: ImageDescriptor
+    """
+
+    images = []
+    if not data:
+        return None
+    if isinstance(data, str) and data.startswith("http"):
+        response = requests.get(data, timeout=settings.request_timeout)
+        response.raise_for_status()
+
+        soup = BeautifulSoup(response.text, "html.parser")
+        for img_tag in soup.find_all("img"):
+            img_url = img_tag.get("src")
+
+            if img_url:
+                # Handle relative URLs
+                img_url = urljoin(data, img_url)
+
+                # Download the image
+                logger.debug("Loading image: {}", img_url)
+                img_response = requests.get(img_url)
+                img_response.raise_for_status()
+                image = Image.open(BytesIO(img_response.content))
+
+                if image_resolution is not None:
+                    image = image.resize(image_resolution)
+
+                # Load image into Pillow
+                images.append(
+                    ImageDescriptor(
+                        url=img_url,
+                        image=image,
+                    )
+                )
+
+        return images